Recurrent neural networks are effective models to process sequences. However, they are
unable to learn long-term dependencies because of their inherent sequential nature. As a …

Artificial intelligence technology in the agricultural sector can reduce carbon emissions from
agrarian activities and revitalize the whole industry. The Indian agricultural sector has …

Hazards are becoming more frequent and disturbing the built environment; this issue
underpins the emergence of resilience-based engineering. Adaptive pathways (APs) were …

Much has been discussed about artificial intelligence's negative environmental impacts due
to its power‐hungry Machine Learning algorithms and CO 2 CO _2 emissions linked to this …

Machine Learning (ML) algorithms have become prevalent in today's digital world. However,
training, testing and deployment of ML models consume a lot of energy, particularly when …

Artificial intelligence (AI) coupled with existing Internet of Things (IoT) enables more
streamlined and autonomous operations across various economic sectors. Consequently …

La inteligencia artificial está suscitando intensos debates en el conjunto de la doctrina sobre
su compatibilidad con la ética humanista imperante en la sociedad occidental. Esta …

AI is playing an important role in promoting sustainable development, but the carbon
footprint caused by AI is scaling quickly and may partly offset the effort to reduce carbon …

Attacks from adversarial machine learning (ML) have the potential to be used" for good":
they can be used to run counter to the existing power structures within ML, creating …

Machine learning (ML) has seen tremendous advancements, but its environmental footprint
remains a concern. Acknowledging the growing environmental impact of ML this paper …